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license: cc-by-sa-4.0 |
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# SLIM-EXTRACT-TOOL |
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<!-- Provide a quick summary of what the model is/does. --> |
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**slim-extract-tool** is a 4_K_M quantized GGUF version of slim-extract, providing a small, fast inference implementation, optimized for multi-model concurrent deployment. |
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This model has been fine-tuned to implement a general-purpose extraction function that takes a custom key as input parameter, and generates a python dictionary consisting of that custom key with the value consisting of a list of the values associated with that key in the text. |
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The intent of SLIMs is to forge a middle-ground between traditional encoder-based classifiers and open-ended API-based LLMs. |
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[**slim-extract**](https://huggingface.co/llmware/slim-extract) is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling. |
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To pull the model via API: |
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from huggingface_hub import snapshot_download |
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snapshot_download("llmware/slim-extract-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False) |
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Load in your favorite GGUF inference engine, or try with llmware as follows: |
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from llmware.models import ModelCatalog |
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# to load the model and make a basic inference |
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model = ModelCatalog().load_model("slim-extract-tool") |
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response = model.function_call(text_sample) |
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# this one line will download the model and run a series of tests |
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ModelCatalog().tool_test_run("slim-extract-tool", verbose=True) |
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Note: please review [**config.json**](https://huggingface.co/llmware/slim-extract-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set. |
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## Model Card Contact |
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Darren Oberst & llmware team |
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[Any questions? Join us on Discord](https://discord.gg/MhZn5Nc39h) |
